U.S. patent application number 13/456214 was filed with the patent office on 2013-10-31 for system, method and program product for providing populace movement sensitive weather forecasts.
This patent application is currently assigned to International Business Machines Corporation. The applicant listed for this patent is Marcos Dias De Assuncao, Bruno Da Costa Flach, Maira Athanazio de Cerqueira Gatti, Takashi Imamichi, Marco Aurelio Stelmar Netto, Raymond Harry Rudy. Invention is credited to Marcos Dias De Assuncao, Bruno Da Costa Flach, Maira Athanazio de Cerqueira Gatti, Takashi Imamichi, Marco Aurelio Stelmar Netto, Raymond Harry Rudy.
Application Number | 20130285820 13/456214 |
Document ID | / |
Family ID | 49476741 |
Filed Date | 2013-10-31 |
United States Patent
Application |
20130285820 |
Kind Code |
A1 |
Assuncao; Marcos Dias De ;
et al. |
October 31, 2013 |
SYSTEM, METHOD AND PROGRAM PRODUCT FOR PROVIDING POPULACE MOVEMENT
SENSITIVE WEATHER FORECASTS
Abstract
A weather forecast system, method of forecasting weather and a
computer program product therefor. A forecasting computer applies a
grid to a forecast area and provides a weather forecast for each
grid cell. Population movement sensors sense population movement in
the area. A swarm detector detects patterns in area population
movement that indicate swarm activity, from which the swarm
detector predicts swarm patterns. A planning module receives area
weather forecasts and swarm patterns, and provides swarm path
indications to the forecasting system for adjusting the grid
applied to the forecast area.
Inventors: |
Assuncao; Marcos Dias De;
(Sao Paulo, BR) ; Flach; Bruno Da Costa;
(Copacabana, BR) ; Gatti; Maira Athanazio de
Cerqueira; (Rio de Janeiro, BR) ; Imamichi;
Takashi; (Kawasaki, JP) ; Netto; Marco Aurelio
Stelmar; (Sao Paulo, BR) ; Rudy; Raymond Harry;
(Yokohama-shi, JP) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Assuncao; Marcos Dias De
Flach; Bruno Da Costa
Gatti; Maira Athanazio de Cerqueira
Imamichi; Takashi
Netto; Marco Aurelio Stelmar
Rudy; Raymond Harry |
Sao Paulo
Copacabana
Rio de Janeiro
Kawasaki
Sao Paulo
Yokohama-shi |
|
BR
BR
BR
JP
BR
JP |
|
|
Assignee: |
International Business Machines
Corporation
Armonk
NY
|
Family ID: |
49476741 |
Appl. No.: |
13/456214 |
Filed: |
April 26, 2012 |
Current U.S.
Class: |
340/601 ;
702/3 |
Current CPC
Class: |
G01W 1/10 20130101 |
Class at
Publication: |
340/601 ;
702/3 |
International
Class: |
G01W 1/10 20060101
G01W001/10; G06F 19/00 20110101 G06F019/00 |
Claims
1. A weather forecast system comprising: a forecasting system
providing a gridded weather forecast for an area; one or more
population movement sensors sensing population movement in said
area; a swarm detector detecting patterns in said population
movement indicating swarm activity and predicting swarm patterns
responsive to said swarm activity; and a planning module receiving
area weather forecasts and said swarm patterns, said forecasting
system adjusting a grid applied to said area responsive swarm path
indications from said planning module.
2. A weather forecast system as in claim 1, wherein said planning
module further receives indications of non-weather related events
and responsive thereto, provides said swarm path indications to
said forecasting system.
3. A weather forecast system as in claim 1, further comprising a
grid correlation detector correlating said predicted swarm patterns
with cells in a current pattern and providing said correlation to
said planning module.
4. A weather forecast system as in claim 1, wherein said planning
module further generates warnings responsive to a revised forecast
based on an adjusted said grid.
5. A weather forecast system as in claim 4, further comprising a
notification system notifying affected population, said planning
module providing said warnings to said notification system for
notifying said affected population.
6. A weather forecast system as in claim 1, wherein said one or
more population movement sensors include at least one fixed
sensor.
7. A weather forecast system as in claim 1, wherein said one or
more population movement sensors include at least one mobile
sensor.
8. A method of forecasting weather, said method comprising:
forecasting weather for an area, said area being overlain with a
populace centric grid segmenting said forecast area into cells,
weather being forecast for each area within each said cell;
monitoring for indications within one or more of said cells of
population swarming to or from one or more different locations;
revising said grid for population swarm responsive to each
indication; re-forecasting weather for said area with said area
being overlain with said revised grid; and once population swarm
has ceased returning to forecasting the weather with a populace
centric grid.
9. A method of forecasting weather as in claim 8, wherein
monitoring for indications comprises monitoring for inclement
weather and swarm associated area behavior.
10. A method of forecasting weather as in claim 9, wherein
responsive to indications of inclement weather, said method
comprises requesting a determination of current and expected
population swarms.
11. A method of forecasting weather as in claim 9, wherein swarm
associated area behavior comprises: indications of a swarm; and
indications of non-weather related events in said area.
12. A method of forecasting weather as in claim 11, wherein
responsive to indications of said swarm, said method comprises:
requesting a determination of a path of a current swarm; and
requesting a current weather forecast along said path.
13. A method of forecasting weather as in claim 11, wherein
responsive to indications of said non-weather related events, said
method comprises requesting a determination of a path of an
expected swarm, said non-weather related events comprising the
occurrence of emergencies.
14. A computer program product for forecasting weather, said
computer program product comprising a non-transient computer usable
medium having computer readable program code stored thereon, said
computer readable program code causing one or more computer
executing said code to: forecast weather for an area, said area
being overlain with a populace centric grid segmenting said
forecast area into cells, weather being forecast for each area
within each said cell; monitor for indications within one or more
of said cells of population swarming to or from one or more
different locations; revise said grid for population swarm
responsive to each indication; re-forecast weather for said area
with said area being overlain with said revised grid; and once
population swarm has ceased return to forecasting the weather with
a populace centric grid.
15. A computer program product for forecasting weather as in claim
14, wherein monitoring for indications comprises said one or more
computer monitoring for inclement weather and swarm associated area
behavior.
16. A computer program product for forecasting weather as in claim
15, wherein responsive to indications of inclement weather, said
one or more computer requests a determination of current and
expected population swarms.
17. A computer program product for forecasting weather as in claim
15, wherein swarm associated area behavior comprises: indications
of a swarm; and indications of non-weather related events in said
area, said non-weather related events including emergencies.
18. A computer program product forecasting weather as in claim 17,
wherein responsive to indications of said swarm, said one or more
computer requests a determination of a path of a current swarm and
a current weather forecast.
19. A computer program product forecasting weather as in claim 17,
wherein responsive to indications of said non-weather related
events, said one or more computer requests a determination of a
path of an expected swarm.
20. A computer program product for forecasting weather, said
computer program product comprising a computer usable medium having
computer readable program code stored thereon, said computer
readable program code comprising: computer readable program code
means for providing a gridded weather forecast for an area;
computer readable program code means for receiving population
information from one or more population movement sensors in said
area; computer readable program code means for detecting movement
patterns indicating swarm activity from said population
information; computer readable program code means for predicting
swarm patterns responsive to said swarm activity; and computer
readable program code means for receiving area weather forecasts
and said swarm patterns, said computer readable program code means
for providing a gridded weather forecast adjusting a weather
forecast grid applied to said area responsive to said swarm path
indications.
21. A computer program product for forecasting weather as in claim
20, further comprising computer readable program code means for
waiting for swarm activity.
22. A computer program product for forecasting weather as in claim
20, further comprising computer readable program code means for
correlating said predicted swarm patterns with cells in a current
pattern.
23. A computer program product for forecasting weather as in claim
20, wherein said computer readable program code means for detecting
movement patterns detect non-weather related events causing
movement in said area.
24. A computer program product for forecasting weather as in claim
20, further comprising computer readable program code means for
generating targeted warnings responsive to a revised forecast based
on an adjusted said weather forecast grid.
25. A computer program product for forecasting weather as in claim
24, further comprising computer readable program code means for
notifying affected population responsive to said targeted warnings.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] The present application is related to U.S. patent
application Ser. No. 13/251,889, (Attorney docket No.
YOR920110554US1), "SYSTEM, METHOD AND PROGRAM PRODUCT FOR PROVIDING
POPULACE CENTRIC WEATHER FORECASTS" to Victor Fernandes Cavalcante
et al., filed Oct. 3, 2011; to U.S. patent application Ser. No.
13/275,313, (Attorney docket No. YOR920110555US1), "SYSTEM, METHOD
AND PROGRAM PRODUCT FOR PROACTIVELY PROVISIONING EMERGENCY COMPUTER
RESOURCES USING GEOSPATIAL RELATIONSHIPS" to Victor Fernandes
Cavalcante et al., filed Oct. 18, 2011; and to U.S. patent
application Ser. No. 13/290,334, (Attorney docket No.
YOR920110556US1), "SYSTEM, METHOD AND PROGRAM PRODUCT FOR FLOOD
AWARE TRAVEL ROUTING" to Victor Fernandes Cavalcante et al., filed
Nov. 7, 2011, all assigned to the assignee of the present invention
and incorporated herein by reference.
BACKGROUND OF THE INVENTION
[0002] 1. Field of the Invention
[0003] The present invention is related to providing weather
forecasts on wide geographic areas, and more particularly, to
adjusting weather forecasts in response to population movement in a
wide geographic area.
[0004] 2. Background Description
[0005] Weather forecasts are based on weather data collected from
sensors that are located over a large geographic area or even
worldwide. In forecasting the weather for a wide geographic area,
the area typically is divided into smaller more manageable units by
a superimposing a grid over the area. Then, the relationship of the
weather data among the several units or grid locations is described
in several algebraic equations, e.g., using a Finite Element Model
(FEM) for the gridded area. Frequently, the FEM requires a
considerable, even excessive, amount of data processing
resources.
[0006] Moreover, the higher the grid resolution, the larger the
number of units, the more complex the FEM equations and,
correspondingly, the more data processing resources consumed in
generating weather forecasts. The data processing demands may be
such that, it may be infeasible to provide real time or even timely
forecasts for all grid locations. This is especially troublesome
when, as is commonly the case, forecast results are subject to
tight delivery deadlines. What is commonly known as adaptive mesh
refinement (AMR) is a type of dynamic mesh refinement that has been
used to selectively provide real time forecasts.
[0007] Adaptive mesh refinement begins with a low resolution grid
for an area. The weather map contains coarse-grained cells to
provide rough initial forecasts. Where more detailed forecasts are
necessary for certain cells, provided there is sufficient data and
time available, those cells are further refined. Typically,
refinement is based on quality and quantity of sensors in the area,
i.e., focus is on areas with more and better sensors. B. Plale et
al., "CASA and LEAD: Adaptive Cyberinfrastructure for Real-Time
Multiscale Weather Forecasting," IEEE Computer Magazine, 2006,
provides an example of sensor based refinement, that focuses grid
refinement on the sensors, i.e. sensor quality and quantity.
However, sensor based refinement may not refine the forecast grid
where people are, much less where they are headed.
[0008] Even when population is considered in forecasting, e.g., by
sensor placement or otherwise, heavy weather, e.g., tornadoes or
hurricanes, or local emergencies, may result in conditions that
cause local evacuations. State of the art forecasting does not
consider these emergencies in gridding an area. How the local
populace evacuates an area, an example of swarm behavior, can vary
widely depending on the situation, population and locale. Since
almost by definition, evacuation means emptying a relatively
densely populated area into relatively empty or less dense, low
population areas, evacuating part of an area tends to render any
current weather forecasts stale and inadequate.
[0009] Thus, there is a need for efficiently providing real time
weather forecasts for large areas with a fluid population
distribution; and, more particularly for efficiently and quickly
adjusting weather forecasts for local population swarm
behavior.
SUMMARY OF THE INVENTION
[0010] A feature of the invention is population swarm adjusted
weather forecasts;
[0011] Another feature of the invention is population swarm
adjusted grid refinement for weather forecasts;
[0012] Yet another feature of the invention is providing weather
forecasts tailored to how a mobile or fluid population in a large
area is reacting to local conditions;
[0013] Yet another feature of the invention swarm adjusted
forecasts tailored to the needs of a mobile or fluid population in
a large area and adapted for how the population is reacting to, or
expected to react to, area inclement weather or area behavior, such
as swarm activity and non-weather related events, e.g.,
emergencies.
[0014] The present invention relates to a weather forecast system,
method of forecasting weather and a computer program product
therefor. A forecasting computer applies a grid to a forecast area
and provides a weather forecast for each grid cell. Population
movement sensors sense population movement in the area. A swarm
detector detects patterns in area population movement that indicate
swarm activity, from which the swarm detector predicts swarm
patterns. A planning module receives area weather forecasts and
swarm patterns, and provides swarm path indications to the
forecasting system for adjusting the grid applied to the forecast
area.
BRIEF DESCRIPTION OF THE DRAWINGS
[0015] The foregoing and other objects, aspects and advantages will
be better understood from the following detailed description of a
preferred embodiment of the invention with reference to the
drawings, in which:
[0016] FIG. 1 shows an example of a swarm behavior detection and
forewarning system according to a preferred embodiment of the
present invention;
[0017] FIG. 2 shows an example of swarm flow and detection of the
local population in an area served by a cellular phone or cell
phone system;
[0018] FIGS. 3A-B show a simple example comparing refinement of a
forecast for a wide area and refinement according to a preferred
embodiment of the present invention;
[0019] FIG. 4 shows an example of operation of a preferred planning
module;
[0020] FIG. 5 shows an example of a preferred traffic optimization
module.
DESCRIPTION OF PREFERRED EMBODIMENTS
[0021] As will be appreciated by one skilled in the art, aspects of
the present invention may be embodied as a system, method or
computer program product. Accordingly, aspects of the present
invention may take the form of an entirely hardware embodiment, an
entirely software embodiment (including firmware, resident
software, micro-code, etc.) or an embodiment combining software and
hardware aspects that may all generally be referred to herein as a
"circuit," "module" or "system." Furthermore, aspects of the
present invention may take the form of a computer program product
embodied in one or more computer readable medium(s) having computer
readable program code embodied thereon.
[0022] Any combination of one or more computer readable medium(s)
may be utilized. The computer readable medium may be a computer
readable signal medium or a computer readable storage medium. A
computer readable storage medium may be, for example, but not
limited to, an electronic, magnetic, optical, electromagnetic,
infrared, or semiconductor system, apparatus, or device, or any
suitable combination of the foregoing. More specific examples (a
non-exhaustive list) of the computer readable storage medium would
include the following: an electrical connection having one or more
wires, a portable computer diskette, a hard disk, a random access
memory (RAM), a read-only memory (ROM), an erasable programmable
read-only memory (EPROM or Flash memory), an optical fiber, a
portable compact disc read-only memory (CD-ROM), an optical storage
device, a magnetic storage device, or any suitable combination of
the foregoing. In the context of this document, a computer readable
storage medium may be any tangible medium that can contain, or
store a program for use by or in connection with an instruction
execution system, apparatus, or device.
[0023] A computer readable signal medium may include a propagated
data signal with computer readable program code embodied therein,
for example, in baseband or as part of a carrier wave. Such a
propagated signal may take any of a variety of forms, including,
but not limited to, electro-magnetic, optical, or any suitable
combination thereof. A computer readable signal medium may be any
computer readable medium that is not a computer readable storage
medium and that can communicate, propagate, or transport a program
for use by or in connection with an instruction execution system,
apparatus, or device.
[0024] Program code embodied on a computer readable medium may be
transmitted using any appropriate medium, including but not limited
to wireless, wireline, optical fiber cable, RF, etc., or any
suitable combination of the foregoing.
[0025] Computer program code for carrying out operations for
aspects of the present invention may be written in any combination
of one or more programming languages, including an object oriented
programming language such as Java, Smalltalk, C++ or the like and
conventional procedural programming languages, such as the "C"
programming language or similar programming languages. The program
code may execute entirely on the user's computer, partly on the
user's computer, as a stand-alone software package, partly on the
user's computer and partly on a remote computer or entirely on the
remote computer or server. In the latter scenario, the remote
computer may be connected to the user's computer through any type
of network, including a local area network (LAN) or a wide area
network (WAN), or the connection may be made to an external
computer (for example, through the Internet using an Internet
Service Provider).
[0026] Aspects of the present invention are described below with
reference to flowchart illustrations and/or block diagrams of
methods, apparatus (systems) and computer program products
according to embodiments of the invention. It will be understood
that each block of the flowchart illustrations and/or block
diagrams, and combinations of blocks in the flowchart illustrations
and/or block diagrams, can be implemented by computer program
instructions. These computer program instructions may be provided
to a processor of a general purpose computer, special purpose
computer, or other programmable data processing apparatus to
produce a machine, such that the instructions, which execute via
the processor of the computer or other programmable data processing
apparatus, create means for implementing the functions/acts
specified in the flowchart and/or block diagram block or
blocks.
[0027] These computer program instructions may also be stored in a
computer readable medium that can direct a computer, other
programmable data processing apparatus, or other devices to
function in a particular manner, such that the instructions stored
in the computer readable medium produce an article of manufacture
including instructions which implement the function/act specified
in the flowchart and/or block diagram block or blocks.
[0028] The computer program instructions may also be loaded onto a
computer, other programmable data processing apparatus, or other
devices to cause a series of operational steps to be performed on
the computer, other programmable apparatus or other devices to
produce a computer implemented process such that the instructions
which execute on the computer or other programmable apparatus
provide processes for implementing the functions/acts specified in
the flowchart and/or block diagram block or blocks.
[0029] Turning now to the drawings and more particularly, FIG. 1
shows an example of a swarm adjusted, population centric weather
forecasting system 100, wherein a forecasting system 102 and a
planning module 104 coactively forecast overall area weather and
adjust weather forecasting constraints based on detected swarm
activity according to a preferred embodiment of the present
invention. An example of a suitable forecasting system 102, that
may be adapted for use in the present invention, is the populace
centric forecasting system described in U.S. patent application
Ser. No. 13/251,889, (Attorney docket No. YOR920110554US1),
"SYSTEM, METHOD AND PROGRAM PRODUCT FOR PROVIDING POPULACE CENTRIC
WEATHER FORECASTS" to Victor Fernandes Cavalcante et al.
(Cavalcante), filed Oct. 3, 2011, assigned to the assignee of the
present invention and incorporated herein by reference. In
Cavalcante the forecasting grid applied to the area was refined
based on population concentration, but not how the crowd might
respond to environmental factors.
[0030] While crowd modeling and simulation has been considered for
application elsewhere, it has not heretofore been applied to
weather forecasting. Examples of prior attempts at such other
applications include: system planning; understanding the behavior
of large crowds and pedestrians; comprehending human motion in
constrained environments; adjusting system capacity to flash crowd
conditions; defining the behavior of characters in games and
movies; and, in implementing motion patterns for robotic systems.
Primarily, these applications and simulations have been used to
approximate crowd behavior. Several techniques have been attempted
including using agent-based simulations, navigation fields and
personality traits. Previously, these techniques were used to help
understand how crowds behave under emergency and evacuation
situations, and for example, for planning locations for doors,
gates and emergency exits.
[0031] However, a preferred system 100 dynamically refines
forecasts for regions of interest, performing an overall area and
cell by cell mesh refinement based on population swarm behavior.
Thus, the system 100 provides a detailed, swarm adjusted custom
weather forecast for planning and responding to local events. The
system 100 detects swarm behavior, predicts and, optionally,
automatically sends information and warnings about current regions
of interest to affected populace and to regional (e.g., city, town,
neighborhood) forecast and planning systems. Thus, forewarned
regional systems can then simulate various local conditions, such
as traffic, and plan expected emergency service deployments, e.g.,
rescue personnel. Thus forewarned, such deployment is better
planned to respond to the conditions causing the population
movement, because the planning is based on swarm activity reflected
in current crowd movement or expected crowd movement within a given
time horizon.
[0032] It should be noted that the flowchart and block diagrams in
the Figures illustrate the architecture, functionality, and
operation of possible implementations of systems, methods and
computer program products according to various embodiments of the
present invention. In this regard, each block in the flowchart or
block diagrams may represent a module, segment, or portion of code,
which comprises one or more executable instructions for
implementing the specified logical function(s). It should also be
noted that, in some alternative implementations, the functions
noted in the block may occur out of the order noted in the figures.
For example, two blocks shown in succession may, in fact, be
executed substantially concurrently, or the blocks may sometimes be
executed in the reverse order, depending upon the functionality
involved. It will also be noted that each block of the block
diagrams and/or flowchart illustration, and combinations of blocks
in the block diagrams and/or flowchart illustration, can be
implemented by special purpose hardware-based systems that perform
the specified functions or acts, or combinations of special purpose
hardware and computer instructions.
[0033] As also shown in the example of FIG. 1, a preferred system
100 includes a notification system 106 notifying users in the local
population 108 of warnings 110 for the area. A swarm detector 112
detects and projects population 108 swarm paths from behavior
detected by sensors 114 in the area. The swarm detector 112 passes
predicted populace location and populace behavior 116 to the
preferred planning module 104. The swarm detector 112 also
redirects any mobile sensors 114 to swarm locations based on
predicted populace, e.g., to regions where the coverage is
otherwise low. A grid/region correlation detector 118 determines a
geospatial-temporal correlation for the area among critical regions
with other regions, correlating the swarm path/behavior with grid
cells. The planning module 104 identifies cells for further
resolution based on this geospatial-temporal correlation, and
passes the determination, e.g., a revised grid, for a refined
forecast to the forecasting system 102. Although shown in multiple
independent computers, it is understood that the subsystems 102,
106, module 104 and detectors 112 may be collocated together in one
or more individual computers. Further, although communications are
shown by direct connection, it is further understood that
communications may be over a network connecting system elements
102-106, 119-118 together.
[0034] Optionally, in addition to forecasting weather related
events, the system 100 can be equipped to warn the local populace
and provide emergency guidance for other types of unscheduled
and/or unexpected events, e.g., earthquakes, gas pipeline leaks,
vehicle accidents, and biological hazards. Once a particular
forecast is complete, the notification system 106 warns the
population 108 of the concerned area(s), e.g., using multiple
different media, individually or, preferably, simultaneously. Those
media can include, but are not limited to, audio media (e.g.,
radio), video media (e.g., television), cellular/text/Internet
Protocol (IP) based media (e.g., chat or text messaging) and social
networking websites (e.g., Facebook and Twitter).
[0035] Broadcasting these warnings willy-nilly to the general
population without careful planning as was done previously, might
otherwise exacerbate the emergency, e.g., by causing traffic jams
that clog escape routes. Advantageously, however, the preferred
system 100 provides decision-makers with a clear picture of
critical areas in real time and a clear indication of how that
picture is likely to change during and after the particular event.
Thus, authorities may work to reduce the occurrence of problems
with unmanaged such events, providing targeted warnings directed
primarily to those that may be affected by the event or the
aftermath of the event.
[0036] FIG. 2 shows an example of swarm flow of the local
population 108 in an area served by a cellular phone or cell phone
system, for application of the preferred embodiment of the example
of FIG. 1. Flow sensors 114 of this example include both fixed
sensors 114-f (e.g., special purpose sensors, cell towers, etc.)
and mobile sensors 114-m, e.g., one or more mobile cell phone
users.
[0037] In a typical cell phone network, the area serviced by the
network is divided into cells, each serviced by a cell phone tower
114-f, and once connected, can place and receive calls. Typically,
cell phones in the particular cell connect to the network through
the respective tower 114-f As each cell phone enters a particular
cell, the phone registers with the tower 114-f, essentially
connecting to the network through the tower 114-f Thus each
particular tower 114-f is aware of all of the connected cell phones
and the connecting cell phones as well, and therefore, the tower is
aware of the cell population and changes in the population.
[0038] The mobile sensors 114-m may be dispatched to regions where
coverage is otherwise low and, further, complement locational
sensing accuracy. The mobile sensors 114-m include both fixed
sensors suitable for monitoring populace movement/mobility in the
immediate location, e.g., video surveillance and public WiFi, and
moveable sensors including, for example, mobile hot spots. Thus,
mobile sensors 114-m can be allocated for regions where fixed
sensor 114-f coverage is low, e.g., in planning for upcoming
weather/events, or on the fly in real time in responding to an
emergency. A suitable standard optimization method, such as
semidefinite programming (SDP) relaxation, may be used to determine
an optimal mobile sensor 114-m allocation to cover a sufficient
portion of a crowd.
[0039] Each cell tracks and reports changes in cell population,
which each cell typically does normally. The cell phone system can
collect population movement data for the entire area based on cell
phone user movement. Thus, with fixed sensor 114-f information
supplemented with sufficient mobile sensor 114-m, that data may be
used to predict swarms as they occur in the area. Thus, application
of the present invention to an area facilitates a responsible area
authority in managing developing/ongoing swarm type behavior in
response to weather related and non-weather related events (e.g.,
emergencies) to a rapid, and much safer, conclusion. Route
recommendation may be verified by comparing a current, determined
crowd path against a predicted path, and moreover, by whether the
traffic flows smoothly in that current path, e.g., is free of
traffic jams.
[0040] A preferred forecasting system 102 provides environmental
forecasts for the original area and refined cells to the planning
module 104. Environmental forecasts may include, for example,
weather forecasts, flooding predictions, and traffic flow
forecasts. The forecasting system 102 forecasts based on, for
example, data collected from weather sensors (not shown) in
combination with computer simulations, e.g., as described by
Cavalcante. The planning module 104 provides the forecasting system
102 with swarm prediction population adjustments for gridding the
area for refining forecasts according to those adjustments.
Alternately, the planning module 104 may revise the grid and
provide the forecasting system 102 with the revised grid. Guided by
the current populace density 116 and the geospatial-temporal
correlation from the grid/region correlation detector 118, the
planning module 104 also generates a series of targeted warnings
110 presented by the warning system 106 to the population 108 of
the affected regions.
[0041] FIGS. 3A-B shows a simple example comparing refinement of a
forecast for a wide area 120, wherein grid refinement is as
described by Cavalcante as compared to swarm detection and
refinement according to a preferred embodiment of the present
invention. In the initial iteration a three by three (3.times.3)
square cell grid is overlaid on wide area 130. In the populace
centric example of FIG. 3A, cells 122, 124 are marked complete
after the first, coarse iteration with cells 126, 128 remaining
unmarked. Cells 126, 128, which may contain, for example, cities,
villages or some other static or transient human activity, are
further refined for a more comprehensive forecast. With each
iteration, unmarked areas may be prioritized by weighting
information from static data (e.g., fixed population and scheduled
events) and dynamic data (e.g., transient population and historic
events) to grid the areas and forecast weather for increasingly
smaller geographic regions.
[0042] So after the first iteration the grid is refined with a set
of sub-regions only for each unmarked cell 126, 128, i.e., a
locally higher resolution 3.times.3 grid. The forecast is further
refined using the more refined grid in a second iteration, with the
forecast complete for refined grid cells 130. In a third and final
iteration, unmarked cells 132 further refine the grid with a still
smaller 3.times.3 grid. The occurrence of an unexpected non-weather
related event, such as an emergency or unexpected movement (e.g.,
the beginning of swarm behavior), would not be considered by
Cavalcante, at least until movement changed the area population
distribution sufficiently to merit changes in one or more
iteration.
[0043] As shown in the example of FIG. 3B, however, a preferred
system 100 reacts to an unexpected event 134 by refining the grid
where a swarm is detected and expected to form in the affected
coarse grid cells 124, 126. In the second iteration, in addition to
refining the grid in cells 126, 128 with a locally higher
resolution 3.times.3 grid, cells 124 adjacent to, or in the area
affected by, the event are refined as well. Further, in the
next/final iteration in addition to a more refined grid in cells
132, the higher resolution, more refined grid is used in cells 136
as well.
[0044] FIG. 4 shows an example of operation 140 of a preferred
planning module 104 according to a preferred embodiment of the
present invention with reference to the system 100 of FIG. 1.
Preferably, the preferred planning module 104 remains dormant,
e.g., waiting 142 until a trigger arrives 144. There are typically
three types of triggers, a behavioral trigger 146 and two types of
event triggers 148, 150. Identified swarm activity triggers 146 the
planning module 104, independent of weather 148 and local activity
150. When there is no swarm activity, the occurrence of inclement
weather 148 and/or emergencies 150 trigger the planning module 104.
The type of trigger 146, 148, 150 determines the planning module
104 response. The planning module 104 responds to both swarm
activity 146 and inclement weather 148 triggers by requesting swarm
behavior from the swarm detector 112 and a weather forecast from
the forecasting system 102 with the order of each request depending
upon the trigger 146, 148. The planning module 104 responds to
emergencies triggers 150 by requesting swarm behavior from the
swarm detector 112, i.e., weather may not be requested.
[0045] The swarm detector 112 detects the current location of area
populace, monitors travel/movement in the general population for
swarm activity, and provides a trigger 146 in response to finding
the initial conditions of a swarm. When a trigger 146 indicates
swarm behavior was detected, the planning module 104 returns a
request 152 to the swarm detector 112 to predict the expected swarm
behavior, e.g., the swarm path.
[0046] Whether monitoring for swarm activity or in response to a
request 152, the swarm detector 112 analyzes the population
movement and predicts trends, e.g., general movement or flow, by
simulating the swarm behavior. Suitable such models for detecting
or predicting swarm behavior and identify population movement
(swarm activity) are described by Tatomir et al., "Hierarchical
Routing in Traffic Using Swarm-Intelligence," Intelligent
Transportation Systems Conference, 2006, ITSC '06, IEEE and
Teodorovic "Transport Modeling by Multi-Agent Systems: a Swarm
Intelligence Approach," Transportation Planning and Technology,
August 2003, Vol. 26, No. 4, pp. 289-312, both incorporated herein
by reference. Optionally, the swarm detector 112 can include
historical data 156 that reflects movement tendencies in predicting
general population response, e.g., movement or flow. When the
general movement or flow indicates swarm behavior, the swarm
detector 112 provides the swarm trigger 146 to the planning module
104.
[0047] The planning module 104 identifies critical areas 158 based
on the predicted swarm behavior, e.g., areas where a large number
of travelers are expected. The planning module 104 requests 160
weather and flooding predictions from the forecasting system 102
for those critical areas, e.g., with the area gridded based on the
projected swarm flow. When the planning module 104 receives 162 the
forecast, the planning module 104 passes the results to a traffic
optimization module 164 to iteratively arrive at an optimal traffic
flow.
[0048] When a trigger 148 indicates flooding or inclement weather
was detected, the planning module 104 returns a request 160 for
weather and flooding predictions from the forecasting system 102
for those critical areas, e.g., with the area gridded based on the
projected swarm flow, e.g., as shown in the example of FIG. 3B.
When the planning module 104 receives 162 the forecast, a request
152 is sent to the swarm detector 112 to predict 156 the expected
swarm behavior. Again, the planning module 104 passes the results
to a traffic optimization module 164 to iteratively arrive at an
optimal traffic flow during the inclement weather and/or any
flooding.
[0049] As noted hereinabove, if the trigger 150 indicates an
emergency is developing, weather is ignored. The planning module
104 returns a request 152 to the swarm detector 112 to predict the
expected swarm behavior 154. The planning module 104 identifies
critical areas 158 based on the predicted swarm behavior, and
passes the results to a traffic optimization module 164 to
iteratively arrive at an optimal traffic flow during the
emergency.
[0050] FIG. 5 shows an example of a traffic optimization module
164. In each iteration a traffic simulator 1640 simulates traffic
in the critical areas. If the simulation iteration 1640 provides
unacceptable results 1642, e.g., traffic jams or dangerous
situations, then the traffic simulation conditions are modified
1644 iteratively to improve traffic flow and/or reroute traffic
around critical areas. For example, high occupancy vehicle (HOV)
lanes may be opened or reversed to facilitate traffic through the
critical areas; two way streets may be converted to one way and the
direction on one way streets may be selectively reversed; traffic
lights may be retimed and resynchronized; warnings may be
retargeted (e.g., to more/fewer area travelers) or otherwise
modified; and emergency response vehicles may be dispatched. The
traffic optimization module 164 reruns the traffic simulation 1640
under the modified conditions and re-checks 1642 until the results
are acceptable. Once the traffic optimization module 164 finds an
acceptable plan 1642 the traffic optimization module 164 institutes
1646 the plan, and in FIG. 4, the planning module 104 returns to
the wait state 142.
[0051] Thus, advantageously, the present invention facilitates
forecast and planning for more accurate, swarm driven results for
higher priority regions, e.g., areas with extreme weather events
have a greater impact on local populace. Forecast resource
prioritization reduces computing and storage resource requirements
for forecasting and simulating weather and emergency effects. Also,
the preferred system 100 provides decision-makers with a clear
picture of critical areas in real time and a clear indication of
how that picture is likely to change during and after the
particular event. Thus, the present invention enables authorities
to reduce the occurrence of problems with unmanaged such events, by
providing targeted warnings directed primarily to those that may be
affected by, or the aftermath of, the event.
[0052] While the invention has been described in terms of preferred
embodiments, those skilled in the art will recognize that the
invention can be practiced with modification within the spirit and
scope of the appended claims. It is intended that all such
variations and modifications fall within the scope of the appended
claims. Examples and drawings are, accordingly, to be regarded as
illustrative rather than restrictive.
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